# evaluate_Weka_classifier

From RWeka v0.4-18
by Kurt Hornik

##### Model Statistics for R/Weka Classifiers

Compute model performance statistics for a fitted Weka classifier.

- Keywords
- models

##### Usage

```
evaluate_Weka_classifier(object, newdata = NULL, cost = NULL,
numFolds = 0, complexity = FALSE,
class = FALSE, seed = NULL, ...)
```

##### Arguments

- object
- a
`Weka_classifier`

object. - newdata
- an optional data frame in which to look for variables
with which to evaluate. If omitted or
`NULL`

, the training instances are used. - cost
- a square matrix of (mis)classification costs.
- numFolds
- the number of folds to use in cross-validation.
- complexity
- option to include entropy-based statistics.
- class
- option to include class statistics.
- seed
- optional seed for cross-validation.
- ...
- further arguments passed to other methods (see details).

##### Details

The function computes and extracts a non-redundant set of performance statistics that is suitable for model interpretation. By default the statistics are computed on the training data.

Currently argument `...`

only supports the logical variable
`normalize`

which tells Weka to normalize the cost matrix so that
the cost of a correct classification is zero.

Note that if the class variable is numeric only a subset of the statistics
are available. Arguments `complexity`

and `class`

are then
not applicable and therefore ignored.

##### Value

- An object of class
`Weka_classifier_evaluation`

, a list of the following components: string character, concatenation of the string representations of the performance statistics. details vector, base statistics, e.g., the percentage of instances correctly classified, etc. detailsComplexity vector, entropy-based statistics (if selected). detailsClass matrix, class statistics, e.g., the true positive rate, etc., for each level of the response variable (if selected). confusionMatrix table, cross-classification of true and predicted classes.

##### References

I. H. Witten and E. Frank (2005).
*Data Mining: Practical Machine Learning Tools and Techniques*.
2nd Edition, Morgan Kaufmann, San Francisco.

##### Examples

```
## Use some example data.
w <- read.arff(system.file("arff","weather.nominal.arff",
package = "RWeka"))
## Identify a decision tree.
m <- J48(play~., data = w)
m
## Use 10 fold cross-validation.
e <- evaluate_Weka_classifier(m,
cost = matrix(c(0,2,1,0), ncol = 2),
numFolds = 10, complexity = TRUE,
seed = 123, class = TRUE)
e
summary(e)
e$details
```

*Documentation reproduced from package RWeka, version 0.4-18, License: GPL-2*

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